In image analysis and processing, image attributes and feature extraction play an important role. These features generally fall into two major categories, i.e., primary and secondary, based on the way these features are analyzed or used by the human visual system in understanding the scene content. One of the prominent primary features used very often for pattern analysis and recognition by the human visual system includes color, for example, dominant color extraction. Hence, a variety of applications such as object classification, representation, query, content-based image, video indexing, video retrieval, medical image analysis, and bio-metrics, for example, derive inputs from accurate color analysis.
Though human eye perceives the dominant color very easily, automatic extraction of dominant color from video and/or image data may be challenging due to a variety of complexities, such as illumination variations, shadows, glossy surfaces, improper data capture, and reflections. The reflections may be caused by light reflecting from vehicle windshields and windows. Further, complexities of extracting dominant color may be challenging when the object includes multiple colors, for example, graffiti written on a side of a taxi. The object color as seen by the camera may change over the object area. The object color may vary depending on velocity and camera angle. The object color may not show-up clearly in case of far-field objects. The object color in case of humans may be arbitrary depending on dress.